Journal of Money Laundering Control — Vol. 2 No. 1

Money Laundering Regulation: The Micro Economics Donato Masciandaro

The analysis of the interactions between the crimi­nal economy and the financial markets has not yet been systematically studied by the economists. This study belongs to a current research interested in this area,1 ie the economic analysis of money laundering. The work is organised as follows.

The first section is devoted to the economic analysis of the relationships between the illegal activities and the the money laundering crime. A microeconomic model is proposed, based on the idea that money laundering has the basic economic role of turning a potential purchasing power into an effective one.

In the second section, we apply the approach and the results of the theoretical analysis to the Italian case. The final section stresses some conclu­sions.

CRIME AND MONEY LAUNDERING: THE MICRO MODEL The existing relationship between the development of illegal markets and the financial system comes from money laundering. The aim is to develop a microanalysis of the money laundering phenom­enon, i.e. the activity that an economic agent car­ries out when he has to hide the criminal nature of a given amount of liquidity.

To understand the crucial role played by money laundering with regard to the illegal economy as a whole, an economic definition of this crime is needed that captures those specific features making money laundering different from all the other criminal economic activities.

Money laundering exists whenever a given potential flow of purchasing power is turned into an effective one. The initial purchasing power is potential because, coming from illegal activities, nobody can use it either for consumption or for investment purposes, without an inceasing likeli­hood of being discovered to be criminal.

Keeping attention upon the concept of trans­forming a potential purchasing power into an effective one allows not only identification of the

key feature of money laundering as criminal activity, but also to stress its importance; many of the agents carrying out either illegal or criminal activities need a money laundering service to make their activities grow, thereby minimising the risks.

Therefore the crucial agent to analyse is the person who needs the clean money (the money launderer): his goal is to turn dirty liquidity, coming from any criminal or illegal activity, into clean money that can be used without risk for consumption, saving, investment in legal sectors, as well as for new investment in illegal markets. The word 'clean' means that the money is free of those traces linking it to the original crimes.

Having defined the issue in general terms, the micro choices of an agent (the criminal) who must decide if and in what amount cleaning the reve­nues of a given crime that he made can be investi­gated; in other words, the determinants of the money laundering demand will be analysed.

The hypothesis is that the criminal can get from an illegal activity a given income flow, equal to w. This income cannot be used immediately because there would be a high probability of exposure. Some expenditure can be made without risk, other with a little bit of risk and the remainder with a very high probability of indiction. Clean liquidity means complete freedom in resource allocation and this is what differentiates it from dirty finan­cial flows. Not all illegal revenues must be neces­sarily cleaned, but clean liquidity has a competitive advantage compared to dirty liquidity; in fact, a cleaned dollar 'is worth more' — from the crimi­nal point of view — than a dirty dollar. That is, the greater use-value of the cleaned dollar reflects, at least potentially, its greater profitability.

The criminal income w is, therefore, only potential earning capacity: without cleaning, it has a lower value. The criminal, first of all, must decide for each dollar of illegal income whether to clean it or not. The clean dollar can be invested in profitable activities without risk of incrimination, while the dirty one can be spent earning a lower profit and/or taking a higher risk of incrimination,

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therefore the hypothesis can be made that the expected value of the latter is lower, or — for the sake of simplicity — is equal to zero.

Calling U the utility function of the criminal, then the expected utility of dirty money can be zero, whatever its amount. That is:

U(w)=0 (1)

If the choice of cleaning money could be without costs, calling γ the amount of illegal income for which the criminal requires the money laundering service, the result would be γ = w.

The activity of money laundering is, however, a crime. Therefore it is characterised by a sanction T and a probability of detecting the crime p. The criminal will face the following dilemma: if I give my dirty money to be cleaned and everything goes well, I will benefit from clean money less the cost of money laundering; if everything does not go well, I will have the legal punishment plus the loss of the liquidity given to be cleaned.

Defining the hypothesis as the benefit B of having clean liquidity, the cost C of money laun­dering operations and the damage in the case of money laundering punishment S, every cleaned dollar can be used without constraint and gaining a profit. The fact that cleaned liquidity γ has a posi­tive expected return can be seen if it is imagined that the monetary value B of the benefit is equal to:

B = (1 + r)γ = mγ (2)

where r is on average the expected rate of return of, legal and/or illegal, investment of cleaned liquidity.

Each operation of money laundering has an opportunity cost, which is represented by the resources that the criminal subject needs to put it into action. The cost of money laundering may have a dual nature: on one hand, there are techni­cal costs, due to the adopted laundering tech­nology; on the other hand, there are the costs of the anti money laundering regulation. Conditions being equal, the cost of the activity of money laun­dering will depend on the effectiveness of the regulation of anti money laundering; the more effective the regulation, the more expensive it will be for the criminals putting the illegal activity into action. Obviously the two types of cost can be correlated in a more or less strict way.

Therefore the cost C of money laundering

operations will be proportional, with a c parameter of proportionality defined between 0 and 1, to the amount of liquidity given to be cleaned:

C=ry (3)

The monetary value of the damage of the money laundering sanctions S must be at least equal, in amount, to the value γ of the cleaned liquidity (for example: the authority confiscates the money). In reality, the damage coming from a money launder­ing sanction is likely to be a multiple of the confis­cated amount, either for the monetary amount of the sanction, or for the intangible damages of that sanction (loss of reputation, and so on). Then the hypothesis can be made that the amount of the sanction value is a multiple of the money laun­dered money detected; or — again for the sake of simplicity — the square value of that amount of money.

Moreover, when the crime is detected, the sanc­tion can be a sentence with a variable rate of effi­ciency and/or strictness. The speed and the way in which the money laundering punishments are exe­cuted can vary a lot, due to influences from struc­tural variables and/or from variables related to national or international institutional settings. The strictness (or its contrary, the permissiveness) in executing the sanctions can be inferred from the variations of a t parameter, therefore:

S = tγ2 (4)

Having defined the terms of the issue at stake, it can be seen that the criminal has a problem in deciding whether to clean money and how much money to clean. The expected utility E can be specified as follows:

E = u[(1-p)(B-C)+p(-C-S)]

E = u[(1-p)(mγ-cγ)+p(-cγ-tγ2)] (5)

The criminal is risk neutral, and his objective function is consistent with those features tradition­ally deemed necessaries in the economic analysis of illegal behaviours; in fact:

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Journal of Money Laundering Control — Vol. 2 No. 1 — Masciandaro

The criminal's utility lowers as the probability of detecting the crime and the strictness of the sanc­tions grow; it grows as the expected return of cleaned liquidity grows. The options for the crimi­nal can be summarised as in Figure 1. The crimi­nal must find the optimum level of liquidity to be cleaned γ*, given that his maximum resources are w. Deriving equation (5) twice with respect to the key variable for the criminal obtains:

The function reaches its optimum level when

The criminal-expected utility, then, depends on the level of money laundered liquidity (Figure 2). First of all it can be observed that the utility value

is positive when the amount of money laundered liquidity is between 0 and:

The critical value γ' gives us the limit beyond which it is obviously optimal for the criminal not to ask for money laundering services. Above γ', the damage coming from the risk of being detected and discovered is so high that the expected utility is negative, so it is preferable to keep the dirty money and to use it for those expenses and uses where the expected value is zero. This result comes from the fact, other things being equal, that the money laundering sanctions amount is a mul­tiple of the liquidity to be cleaned; therefore, as the liquidity amount grows the damages coming from crime detection grow more than proportionally.

The critical value γ' must, obviously, be com­pared with the given level of illegal resources w. If y' <w (Figure 1), the amount of resources (w— γ'), will be, a priori, excluded from any decision about money laundering activity. But if y'>2w, for all illegal activities the money laundering operation is potentially convenient. Now the effective optimal level γ* can be determined.

The critical value γ' — ie the money-laundering propensity (in absolute value or, if divided by w, in relative value) — will depend on the structural parameters of the model. In fact:

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Looking at the reactivity of the money-laundering propensity to the incrimination probability and the sanctions strictness, more efficient (growing p) and/or strict (growing t) money laundering regula­tions will reduce the money laundering propensity. On the contrary, a growth in the return of the economic activities needing clean liquidity (grow­ing m) makes the money laundering propensity grow; the same happens if the costs of money laundering operations reduce (declining c).

Having defined the relevant variables for crimi­nal choices of demand for money laundering activities, the optimum level γ* can be determined; from equation (7):

with the constraint:

γ* <w (12)

As with the potential propensity to demand money laundering operations, and also for the optimal level of money to be cleaned, relations with the

structural variables of the model can be deter­mined. First, the amount of liquidity to be cleaned is negatively related to the probability of detecting the crime (Figure 3). The higher the probability of detecting the money laundering crime, the lower the level of this criminal activity. It is interesting to see that the liquidity to be cleaned becomes zero when the incrimination probability is high (p = (m—c)/m), but not at its maximum level (p = 1). This result derives from the fact that money laundering activities have an economic cost that, added to the incrimination risk, makes it inconvenient in absolute terms, even without having a maximum level of probability of detecting the crime. In fact, only if there were no cost (c = 0) would the money laundering activity become zero when p = l.

In connection with the illegal resources con­straint w is obtained with the minimum value of the probability of detection (p = (m — c)/(m + 2tw) that matters for the analysis; this means that other lower values — maybe even equal to zero — of probability do nothing to the level of money being cleaned.

Furthermore money laundering is influenced by level of penalty severity (Figure 4). The stricter the authority, the less convenient the money launder­ing operation. For the severity level of penalty there is a minimum level {t = [m(1 — p)— c]/(2pw)} using the illegal resources constraint.

Money laundering activity will depend then, on the return of the cleaned money (Figure 5). It has

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Journal of Money Laundering Control — Vol. 2 No. 1 — Masciandaro

already been seen that the criminal decides to clean money depending on the relative return of the cleaned money with respect to the dirty one. The higher the return, the more convenient it will be to ask for money laundering services. If the return of the cleaned money tends to fall (for example because the opportunity for using dirty money for consumption and investment expenses grows, without risk of incrimination) the incentive for using money laundering services falls. In particu­

lar, the amount of money being cleaned becomes zero if

In this case, given the illegal resources constraint, the maximum value of the return of cleaned liquidity is equal to [m = (c + 2ptw)/(1—p)].

Finally, it is possible to analyse the relationship

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between the cost of money laundering services and the amount of liquidity to be cleaned, ie the money laundering demand in a strict sense, where the goods in demand is the money laundering ser­vice and the price is its cost (Figure 6).

The relationship price-quantity is obviously inverse and the sensibility of the demand of money laundering services with respect to their price, ie elasticity, is equal to:

The elasticity of the money laundering demand changes along the curve: it grows as the cost grows (from zero to infinity) and it gets a one-value in γ**=c/(2pt) point. The money-l;aundering activity is zero when the price is c = m(1 — p), while with a

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Journal of Money Laundering Control — Vol. 2 No. 1 — Masciandaro

price equal to zero the optimum level of money-laundering would be γ*=[m(1 — p)]/(2pt). to be precise, it is necessary to take into account the illegal resources constraint and, where it matters, the minimum value of the price is not zero, but c = m(l — p) — 2ptw.

In addition, the position of the money launder­ing demand curve changes as the structural param­eters of the model change. A growth in the return of the money cleaned produces an upward shift of the curve, while a growth of the probability of indiction and of the severity of the penalties pro­duces downward shifts of the curve.

CRIME, MONEY LAUNDERING AND REGULATION: FROM MICRO TO MACRO Having defined the above micro foundations, it can be shown that, in a given economy, the total volume of economic activity that concerns criminal agents can be linked to the growth of money laun­dering activity, and vice versa. This analysis can be examined by using the traditional multiplier approach in a new framework, the Masciandaro 1996 model.

Assuming that, in the economic system, n homogeneous criminal agents exist; each of them owns w illegal revenues, and a fraction γ of them, equal to the ratio between w and γ* — then the illegal resources constraint holds — needs to be laundered. The total amount of illegal revenues is equal to W.

It is known that the opportunity cost of the money laundering C is in steady proportion to the amount of funds which have to be laundered (c), then:

C=cγW

If the money laundering activity is effective — ie there is no detection of crime — after the first operation, the criminal agent can use the clean liquidity (1 — c)γW, in consumption, saving and investment, both in legal as well as illegal markets. Assuming that a proportion q of the clean liquidity is reinvested in the illegal markets, q is the share of laundered funds that is reinvested in illegal activi­ties, and r is the expected net real return of the investment in illegal markets. For the sake of gen­erality, it can be supposed that funds must be laundered only in part to be reinvested in the ille­

gal sector, so the positive parameter a indicates the share of illegal reinvestment that needs clean liquidity.

After the illegal reinvestment there is a new dirty flow of illegal liquid funds that must be laun­dered, and assuming the behaviour of all criminal agents to be the same, it will be equal to q(1 - c)(1 + r)γ2W.

The crucial hypothesis is that both the allowed consumption and investment in the legal markets and part of the illegal investment need to be financed by clean liquidity. This second hypothesis can be supported by the presence of informed and rational operators on the services' supply to the criminal subject side, or by the behaviour of the criminal, who wants to minimise totally the prob­ability of being discovered.

Repeating in this way the operation of money laundering for an infinite number of times — for steady and normal values of the parameters2 — the global amount of the illegal financial flows Y*, which are the aggregate results of the money laun­dering, will be equal to:

with 0 < q, γ < l. The flow Y* represents the growth of money

laundering, given an initial flow of illegal revenues W, while f is the multiplier. The multiplying effect of money laundering will be the bigger:

(a) the lower the opportunity cost of the money laundering; ie the lower it is, conditions being equal, the effectiveness of regulation:

This result is obviously the same obtained in the micro analysis. If the features of the regu­lation weight heavily on the cost of the illegal activity, when an effective system of rules tends to reduce the capacity of criminal agents to sterilise the illegal liquidity;

(b) the bigger is the share of reinvestment in ille­gal activity:

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(c) the bigger is the expected real return of the activities that need cleaned money:

These results are also consistent with the micro analysis; (d) the bigger is the initial volume of the revenues

of the illegal economic activity;

At the micro level and in the initial stage of the process, the money laundering activity is a fraction of the illegal revenues, at the macro level and in a dynamics perspective it becomes a multiplier of the criminal sector.

In fact, the more effective the action of money laundering, the bigger will be the flows of liquidity at the criminal agent's disposal for reinvestment. In fact, the total amount of liquidity, due to the laun­dering effect, which is fungible for reinvestment R is equal to:

If the multiplier process of money laundering is stable, changes in the revenues of the initial crimi­nal activity will cause a more than proportional effect on the volume of illegal financial activities. The highest multiplying effect is possible when the regulation is ineffective and the technical costs are negligible (c = 0); in this case the degree of the expansion of the volume of the money laundering activity Y* — which coincides with the highest flow of liquidity disposable for reinvestment — is:

The highest value of the multiplier of money laun­dering stresses the fundamental role of the initial illegal economic activity. So it can be concluded that the expected volume of the activity of money laundering grows with the increase of the entity of the criminal activity, as it has a multiplying effect on this latter.

Clearly, the more valid is the starting hypothesis

of a closed economy, so that the real transactions have a financial counterpart in the same economic system, the stronger are these conclusions.

CRIME AND MONEY LAUNDERING IN A GIVEN REGULATORY FRAMEWORK: THE CASE OF ITALY The first two sections have shown proposals for a theoretical analysis of the relationships between criminal activities and money laundering. With a given institutional setting (ie the parameter p and T are constant) and money laundering techniques (ie the price of money laundering services c is constant) the model can be used in order to give an interpretative key to the evolution of the Italian case.

In 1991 the definition of the first anti money laundering rules can be connected to the aware­ness, by the authorities, of a strong expansion of the economic activity of illegal markets in Italy, and in particular of the money laundering

Table 1: Bank deposits, legal economy and crime: correlation matrix (average per capita 1980-90)

Italy Deposits GNP Crimes

North Italy Deposits GNP Crimes

Middle Italy Deposits GNP Crimes

South Italy Deposits GNP Crimes

South Italy with Mafia Deposits GNP Crimes

Deposits

1 0.88 0.27

1 0.71 0.34

1 0.80 0.37

1 0.17 0.51

1 0.36 0.62

GNP

1 0.16

1 0.16

1 0.31

1 0.12

1 0.716

Crimes

1

1

1

1

1

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Journal of Money Laundering Control — Vol. 2 No. 1 — Masciandaro

Table 2: Bank deposits, legal economy and crime (average per capita 1980-90) (dependent variable: bank deposits)

Independent variables Intercept

GNP

Crimes

Interest rates differential -0.69

- 2 R SE Prob>F No. observatiaons

Italy

-0.41 (1.41) 0.68

(0.03)** 0.03

(0.01)**

-0.31 (0.27)**

0.80 1.22 0.00

95

North

-3.37 (1.96) 0.75

(0.13)** 0.04

(0.01)**

-2.41 (0.38)

0.58 0.87 0.00

33

Middle

6.41 (4.08) 0.59

(0.14)** 0.04

(0.02)**

0.68 (4.08)

0.72 1.19 0.00

32

South

-0.13 (2.32) 0.01

(0.05) 0.03

(0.01)**

0.37 (0.52)

0.24 0.58 0.01

30

South with Mafia

2.2 (2.9)

-0.13 (0.18) 0.04

(0.01)**

(0.65)

0.31 0.57 0.02

22

Standard errors in parentheses. A* (**) denotes significance at the 5% (1%) level. Each equation is estimated by OLS. Sources: Bollettino Statistico Banca D'Italia, Annuario ISTAT, Istituto Guglielmo Tagliacarne; various years.

industry. At the same time, more dangerous crimi­nal markets developed: expansion of the power of the mafias throughout the 1980s was seen in the increase in the number of criminal organisations and their membership, in a growth of their terri­torial base, in the range of their activities, as well as of killing and other crimes.3 How can these phenomena be analysed?

The approaches presented above have important predictions regarding medium-term relationships between the growth of illegal and criminal markets and the volume of money laundering. The approach predicts that, through money laundering services, the financial aggregates are, ceteris paribus, larger in countries with more developed illegal markets and with bigger criminal organisations. And this seems to be the case in the Italian situa­tion before 1991: in the stable features of the legis­lation, as well as in money laundering technologies. The key idea is that, in a given eco­nomy, the finacial side reflects not only legal trans­actions, but also illegal ones.

To test this hypothesis a cross-section analysis was carried out on the relationships between bank deposits, the legal economy and illegal markets in 95 Italian districts in the 1980s. The bank deposits reflect in general the features of the Italian finan­cial system, and in particular those of South Italy.4

'Legal' and 'illegal' economies are rather vague concepts which are difficult to quantify. A possible solution for measuring the legal economy is to consider the gross national product per capita. Data on the illegal markets are obviously not available; a proxy of the illegal economy can be the number of crimes per capita: a change in the rate of crime results in a change in the value of the illegal trans­action, ie in the size of the illegal market. Table 1 shows that in Italy the distribution of the legal economy has no correlation with the illegal one.

How do these legal and illegal attributes relate to the features of the banking system? Table 1, which performs cross-area comparisons, provides a clear answer. The table reports correlations between bank deposits and legal and illegal variables in dif­ferent areas. Italy, North Italy, Middle Italy, South Italy, South Italy with Mafia (Campania, Puglia, Calabria, Sicilia). Bank deposits are strongly posi­tively correlated to the legal economy in Italy, North Italy and Middle Italy, while the same vari­able is more positively correlated to the illegal eco­nomy in South Italy, with or without criminal organisations.

The same results are obtained in Table 2. Bank deposits are regressed on two variables (plus a price variable, given by the differential between the deposit interest rate and the government bond

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interest rate). First, the overall fit of the regression improves by introducing the illegal variables. Secondly, only the illegal variable has a positive and significant coefficient at any time. Finally, the impact of the illegal economy is relatively more significant in South Italy.

These findings are then consistent with the criminological, institutional and legal studies that stressed the increasing relationships between the growth of the illegal and criminal activities and the role — more or less conscious — of the banking and financial intermediaries in the money laundering business.5

CONCLUSIONS The theoretical analysis developed in this work led to some conclusions through a formal model. First, the illegal economic activity represented by money laundering plays a crucial role in the growth of illegal markets: the better its efficacy, the more the liquidity flows available to the reinvest­ment of revenues in legal and illegal activities. At the same time the more widespread those crimes that need money laundering services, the bigger the growth of the supply of those services. So the criminal economy and the financial one tend to strengthen each other.

REFERENCES (1) Masciandaro, D. (1995) 'Money Laundering, Banks and

Regulators: An Economic Analysis', IGIER Working Paper

Series, Bocconi University, n. 73; (1996) 'Pecunia olet? Microeconomics of Banking and Financial Laundering', International Review of Economics and Business, October, pp. 817-844; (1988) 'Money Laundering: the Economics', European Journal of Law and Economics (forthcoming).

(2) The money laundering multiplier obviously holds if 1 - γq(1 - c(1 + r) < 1.

(3) Savona, E. U. (ed.) (1992) 'Mafia Issues', International Conference, International Scientific and Professional Advisory Council of the United Nations (ISPAC), Palermo, Italy.

(4) Faini, R., Galli, G. and Giannini, C. (1993) 'Finance and Development: The Case of Southern Italy', in A. Gio-vannini (ed.), 'Finance and Development: Issues and Experience', CEPR, Cambridge University Press, Cam­bridge.

(5) Bosworth Davis, R. and Saltmarsh, G. (1994) 'Money Laundering', Chapman and Hall, London; Norton, J. J. (ed.) (1994) 'Banks: Fraud and Crime', Lloyd's of London Press, London; Savona, E. U. and De Feo, M. A. (1994) 'Money Trails: International Money Laundering Trends and Prevention/Control Policies', in E. U. Savona (ed.), 'Responding to Money Laundering', Harwood Academic, Amsterdam.

Donato Masciandaro is Professor of Monetary Economics, Paolo Baffi Centre for Monetary and Financial Economics, Bocconi University, Milan.

The first draft of this paper was presented at the Eleventh World Congress International Economic Association, Tunis, 21st December, 1997, Session 19: Economics of Corruption and Crime. The author would like to thank all the participants of the session, and in particular Stefano Zamagni, Gianluca Fiorentini, Frederic Rychen and Arindam Dasgupta for helpful comments. He also thanks the Paolo Baffi Centre for its financial support.

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